Hackathon additions #99
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| # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] | ||
| train: /colon_data/polyps_small/images/train | ||
| val: /colon_data/polyps_small/images/valid | ||
| test: /colon_data/polyps_small/images/valid # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 | ||
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| # number of classes | ||
| nc: 1 | ||
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| # class names | ||
| names: [ 'polyp'] | ||
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There was a problem hiding this comment. I didn't dive to deep but I found this notebook a bit confusing at a glance... It seems to be the function If the notebook is converting segmentation masks to bounding boxes I think the function names and docstrings/comments should state this more clearly. Also I think most of the recommendations for the |
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Number of items: 2\n", | ||
| "Item 1: x_min=0, y_min=0, x_max=100, y_max=105\n", | ||
| "Item 2: x_min=144, y_min=225, x_max=193, y_max=278\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import cv2\n", | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "def find_connected_components(image_path):\n", | ||
| " # Read the image\n", | ||
| " image = cv2.imread(image_path)\n", | ||
| " \n", | ||
| " # Convert the image to grayscale\n", | ||
| " gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n", | ||
| " \n", | ||
| " # Threshold the image to create a binary image\n", | ||
| " _, binary = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY)\n", | ||
| " \n", | ||
| " # Find connected components\n", | ||
| " num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(binary, connectivity=8)\n", | ||
| " \n", | ||
| " # Initialize a list to store bounding boxes\n", | ||
| " bounding_boxes = []\n", | ||
| " \n", | ||
| " for i in range(1, num_labels): # Skip the background label 0\n", | ||
| " x_min = stats[i, cv2.CC_STAT_LEFT]\n", | ||
| " y_min = stats[i, cv2.CC_STAT_TOP]\n", | ||
| " width = stats[i, cv2.CC_STAT_WIDTH]\n", | ||
| " height = stats[i, cv2.CC_STAT_HEIGHT]\n", | ||
| " x_max = x_min + width - 1\n", | ||
| " y_max = y_min + height - 1\n", | ||
| " \n", | ||
| " bounding_boxes.append((x_min, y_min, x_max, y_max))\n", | ||
| " \n", | ||
| " return num_labels - 1, bounding_boxes # Subtract 1 to exclude the background\n", | ||
| "\n", | ||
| "# Example usage\n", | ||
| "image_path = '../images/tst_seg_tst_seg500.png'\n", | ||
| "num_items, bounding_boxes = find_connected_components(image_path)\n", | ||
| "print(f\"Number of items: {num_items}\")\n", | ||
| "for i, (x_min, y_min, x_max, y_max) in enumerate(bounding_boxes):\n", | ||
| " print(f\"Item {i+1}: x_min={x_min}, y_min={y_min}, x_max={x_max}, y_max={y_max}\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "ename": "SyntaxError", | ||
| "evalue": "invalid syntax (2760322440.py, line 44)", | ||
| "output_type": "error", | ||
| "traceback": [ | ||
| "\u001b[1;36m Cell \u001b[1;32mIn[3], line 44\u001b[1;36m\u001b[0m\n\u001b[1;33m num_items, boundinimport cv2\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import cv2\n", | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "def find_connected_components(image_path, output_file):\n", | ||
| " # Read the image\n", | ||
| " image = cv2.imread(image_path)\n", | ||
| " \n", | ||
| " # Convert the image to grayscale\n", | ||
| " gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n", | ||
| " \n", | ||
| " # Threshold the image to create a binary image\n", | ||
| " _, binary = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY)\n", | ||
| " \n", | ||
| " # Find connected components\n", | ||
| " num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(binary, connectivity=8)\n", | ||
| " \n", | ||
| " # Initialize a list to store bounding boxes\n", | ||
| " bounding_boxes = []\n", | ||
| " \n", | ||
| " for i in range(1, num_labels): # Skip the background label 0\n", | ||
| " x_min = stats[i, cv2.CC_STAT_LEFT]\n", | ||
| " y_min = stats[i, cv2.CC_STAT_TOP]\n", | ||
| " width = stats[i, cv2.CC_STAT_WIDTH]\n", | ||
| " height = stats[i, cv2.CC_STAT_HEIGHT]\n", | ||
| " x_max = x_min + width - 1\n", | ||
| " y_max = y_min + height - 1\n", | ||
| " \n", | ||
| " # Convert to center coordinates and dimensions\n", | ||
| " x_center = x_min + width / 2\n", | ||
| " y_center = y_min + height / 2\n", | ||
| " \n", | ||
| " bounding_boxes.append((x_center, y_center, width, height))\n", | ||
| " \n", | ||
| " # Write results to a text file\n", | ||
| " with open(output_file, 'w') as f:\n", | ||
| " for x_center, y_center, width, height in bounding_boxes:\n", | ||
| " f.write(f\"0 {x_center:.8f} {y_center:.8f} {width:.8f} {height:.8f}\\n\")\n", | ||
| " \n", | ||
| " return num_labels - 1, bounding_boxes # Subtract 1 to exclude the background\n", | ||
| "\n", | ||
| "# Example usage\n", | ||
| "image_path = '../images/tst_seg_tst_seg500.png'\n", | ||
| "output_file = '../labels/bounding_boxes.txt'\n", | ||
| "num_items, bounding_boxes = find_connected_components(image_path, output_file)\n", | ||
| "print(f\"Number of items: {num_items}\")\n", | ||
| "for i, (x_center, y_center, width, height) in enumerate(bounding_boxes):\n", | ||
| " print(f\"Item {i+1}: x_center={x_center}, y_center={y_center}, width={width}, height={height}\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Number of items: 2\n", | ||
| "Item 1: x_center=0.09115523465703972, y_center=0.11830357142857142, width=0.18231046931407943, height=0.23660714285714285\n", | ||
| "Item 2: x_center=0.30505415162454874, y_center=0.5625, width=0.09025270758122744, height=0.12053571428571429\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import cv2\n", | ||
| "import numpy as np\n", | ||
| "import os\n", | ||
| "\n", | ||
| "def find_connected_components(image_path, output_dir):\n", | ||
| " # Read the image\n", | ||
| " image = cv2.imread(image_path)\n", | ||
| " \n", | ||
| " # Get image dimensions\n", | ||
| " image_height, image_width = image.shape[:2]\n", | ||
| " \n", | ||
| " # Convert the image to grayscale\n", | ||
| " gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n", | ||
| " \n", | ||
| " # Threshold the image to create a binary image\n", | ||
| " _, binary = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY)\n", | ||
| " \n", | ||
| " # Find connected components\n", | ||
| " num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(binary, connectivity=8)\n", | ||
| " \n", | ||
| " # Initialize a list to store bounding boxes\n", | ||
| " bounding_boxes = []\n", | ||
| " \n", | ||
| " for i in range(1, num_labels): # Skip the background label 0\n", | ||
| " x_min = stats[i, cv2.CC_STAT_LEFT]\n", | ||
| " y_min = stats[i, cv2.CC_STAT_TOP]\n", | ||
| " width = stats[i, cv2.CC_STAT_WIDTH]\n", | ||
| " height = stats[i, cv2.CC_STAT_HEIGHT]\n", | ||
| " \n", | ||
| " # Convert to center coordinates and dimensions\n", | ||
| " x_center = x_min + width / 2\n", | ||
| " y_center = y_min + height / 2\n", | ||
| " \n", | ||
| " # Convert to ratios\n", | ||
| " x_center /= image_width\n", | ||
| " y_center /= image_height\n", | ||
| " width /= image_width\n", | ||
| " height /= image_height\n", | ||
| " \n", | ||
| " bounding_boxes.append((x_center, y_center, width, height))\n", | ||
| " \n", | ||
| " # Create the output file path\n", | ||
| " base_name = os.path.basename(image_path)\n", | ||
| " output_file = os.path.join(output_dir, os.path.splitext(base_name)[0] + '.txt')\n", | ||
| " \n", | ||
| " # Write results to a text file\n", | ||
| " with open(output_file, 'w') as f:\n", | ||
| " for x_center, y_center, width, height in bounding_boxes:\n", | ||
| " f.write(f\"0 {x_center:.8f} {y_center:.8f} {width:.8f} {height:.8f}\\n\")\n", | ||
| " \n", | ||
| " return num_labels - 1, bounding_boxes # Subtract 1 to exclude the background\n", | ||
| "\n", | ||
| "# Example usage\n", | ||
| "image_path = '../images/tst_seg_tst_seg500.png'\n", | ||
| "output_dir = '../labels'\n", | ||
| "num_items, bounding_boxes = find_connected_components(image_path, output_dir)\n", | ||
| "print(f\"Number of items: {num_items}\")\n", | ||
| "for i, (x_center, y_center, width, height) in enumerate(bounding_boxes):\n", | ||
| " print(f\"Item {i+1}: x_center={x_center}, y_center={y_center}, width={width}, height={height}\")" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "pgta", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.10" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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| This is an example pipeline on how to convert output from Blender Randomiser to COCO format labelling in preperation of fine tuning polyps detection using YOLOv7 | ||||||||||||||
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| # Data Preperation | ||||||||||||||
| Once the segmentation of polyps are obtained. Use find_bounding_box.ipynb to output the bounding box information as text files. | ||||||||||||||
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| show_bbox.ipynb can be used to show if the correct bounding boxes have been converted. | ||||||||||||||
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| # IMPORTANT! | ||||||||||||||
| **<span style="color:red"> | ||||||||||||||
| When Google Colab session finishes, ALL data is wiped, please save the models! | ||||||||||||||
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| </span>** | ||||||||||||||
| ## To tune the model using custom data: | ||||||||||||||
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There was a problem hiding this comment. Should this title maybe be "Fine-tuning a model locally"? and then the next section "Fine-tuning a model via Google colab"? I think that would be a bit more clear |
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| - clone yolov7 git and install it https://github.com/WongKinYiu/yolov7; pip install -r requirements.txt | ||||||||||||||
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| - log into wandb account (your own API - see below) DO NOT SHARE! | ||||||||||||||
| - run python training script | ||||||||||||||
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| ```python train.py --epochs 100 --device 0 --entity colon_coders --workers 8 --batch-size 32 --data /content/colon.yaml --img 512 512 --cfg /content/yolov7_training_config.yaml --weights '/content/yolov7_training.pt' --name yolov7-colon --hyp data/hyp.scratch.custom.yaml``` | ||||||||||||||
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| ## Google colab instructions | ||||||||||||||
| - upload polyps.zip to google drive | ||||||||||||||
| - upload colon.yaml, yolov7_training.pt, yolov7_training_config.yaml to google drive | ||||||||||||||
| - open google colab and mount drive | ||||||||||||||
| - unzip polyps.zip | ||||||||||||||
| ```python | ||||||||||||||
| import zipfile | ||||||||||||||
| with zipfile.ZipFile("/content/drive/MyDrive/polyps.zip", 'r') as zip_ref: | ||||||||||||||
| zip_ref.extractall("/content/colon_data") | ||||||||||||||
| ``` | ||||||||||||||
| - Important: remove the cache files (otherwise the model will use the cache file to load the data which has the incorrect file paths) | ||||||||||||||
| - could use to code in show_bbox.ipynb to see if data and bounding boxes has been loaded correctly | ||||||||||||||
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| - install yolo7 | ||||||||||||||
| ```python | ||||||||||||||
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There was a problem hiding this comment. Maybe this snippet can go above? (the info is also repeated, so maybe you can just refer here to the steps above) |
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| !git clone https://github.com/WongKinYiu/yolov7 | ||||||||||||||
| %cd yolov7 | ||||||||||||||
| !pip install -r requirements.txt | ||||||||||||||
| ``` | ||||||||||||||
| - set up wandb | ||||||||||||||
| ```python | ||||||||||||||
| !pip install wandb | ||||||||||||||
| import wandb | ||||||||||||||
| wandb.login() | ||||||||||||||
| ``` | ||||||||||||||
| - tune model: make sure colon.yaml has the correct file paths for data, also make sure --data, --cfg and --weights has the correct file paths | ||||||||||||||
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Could this bit me removed? |
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| ```python train.py --epochs 100 --device 0 --entity colon_coders --workers 8 --batch-size 32 --data /content/colon.yaml --img 512 512 --cfg /content/yolov7_training_config.yaml --weights '/content/yolov7_training.pt' --name yolov7-colon --hyp data/hyp.scratch.custom.yaml``` | ||||||||||||||
| - When training is finished, model output is saved under yolov7/runs/train | ||||||||||||||
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| ## Run on test data | ||||||||||||||
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If the evaluation produces metrics (mAP or equivalent) maybe it would be useful to mention it here. |
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| !python test.py --data /content/colon.yaml --img 512 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights runs/train/yolov7-colon2/weights/best.pt --name yolov7_colon_val | ||||||||||||||
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| ## Notes | ||||||||||||||
| - The data location is specified in the config file colon.yaml | ||||||||||||||
| - Training config is specified in yolov7_training_config.yaml | ||||||||||||||
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| ## Weights and Biases | ||||||||||||||
| Weights and Biases is a very good tool to use to track training progress. YoloV7 uses this tool and it is very easy to set up | ||||||||||||||
| - https://wandb.ai/site/research sign up for the free account | ||||||||||||||
| - log into your account, go to top right, under your name select "user profile" | ||||||||||||||
| - go to section "danger zone" and reveal your API code | ||||||||||||||
| - this code is then used to log in to wandb when prompted | ||||||||||||||
| - when you finish you can change the API or throw it away. | ||||||||||||||
| - when training is in progress, go to WandB website, click on top left, you should see the project YOLO which will show the current training session | ||||||||||||||
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Shouldn't test and validation be different splits?
If it is intended maybe it's nice to add a comment explaining why.